Fifteen percent of babies born with a weight less than 1500 g die before being discharged from the hospital and 2 to 5% die within two years from complications. Those infants who survive two years are frequently disabled and prone to a lifetime of multiple health risks. These babies are labeled as very low birth weight (VLBW) but are also considered preterm. The combined conditions affect African-American babies more than twice as commonly as Caucasian babies. The causes are multi-factorial and include genetic susceptibility, environmental exposures and personal behavior risk factors. All three groups of factors are considered as contributory to the racial disparity. A critical need exists in parsing these confounded components. We propose an approach to model these effects that has been made possible by recent developments in Bayesian risk modeling as implemented by Markov Chain Monte Carlo. The study will evaluate the extent for which VLBW belongs in a broader cluster of adverse birth outcomes (ABO) and will identify high-risk locations and any spatial patterns of race-to-race variability. The results will be crucial for designing and justifying the locations and diseases to evaluate in a forthcoming R01 application. We will be proposing a study that employs a genome wide association study (GWAS) to further parse individual risk factors into genetic and behavioral in the presence of hierarchical or geographic risks. The objective of this application is to use an existing database to evaluate very low birth weight as an adverse birth outcome that is potentially correlated to infant cancers. The central hypothesis is VLBW has similar risk patterns to childhood cancer around federal superfund sites in Texas. This hypothesis will be tested by two specific aims: Specific Aim 1 will model the geographic risks for very low birth weights around the 47 federal superfund sites in Texas. Specific Aim 2 will model the spatial correlation among risks for very low birth weight and childhood cancer around the 47 federal superfund sites in Texas. This proposal is innovative because it will exploit two new developments, fully conditional hierarchical modeling and Multivariate modeling. Demonstration of correlation between very low birth weights and infant cancer would validate a powerful extension to a future genome-wide association study. With the results of this study, a future GWAS could parse personal risk factors into genetic and behavioral when confounding and interaction with geographic risk factors is possible. . PUBLIC HEALTH RELEVANCE: The proposed research is significant because the inclusion of a common condition, very low birth weight, with relatively rare childhood cancers in a common cluster of diseases would greatly strengthen future investigations. A critical need exists in parsing the confounded components of race-based genetics, race-based exposures and racial behavioral differences. We propose an approach to model these effects that has been made possible by recent developments in Bayesian risk modeling as implemented by Markov Chain Monte Carlo. These advances include both Multivariate and hierarchical modeling. The results will be crucial for designing and justifying the locations and diseases to evaluate in a forthcoming application.